Building Shadow Research Articles

Impact of urban morphology on the microclimate around elementary schools: A case study from Japan

Urban morphology plays an increasingly important role in changes in the urban microclimate. However, few studies have focused on how such changes are exhibited in areas surrounding elementary schools. This research selected 12 elementary schools in a highly urbanized area as research objects. According to the land use/land cover data, all areas were divided into four categories: commercial districts, high green areas, middle and low-rise residences, and public places. Based on field measurements and ENVI-met simulations, this study attempted to clarify the impact of urban morphology on the microclimate around elementary schools. The results indicated the following: (1) The measured air temperature trends of schools in the same category were the same throughout the day, and there were significant differences between different categories. (2) The building density, floor area ratio, green plot ratio, impervious ground surface fraction, and sky view factor were important urban morphology parameters that affected the microclimate around the elementary school. (3) Building shadow reduced the air temperature, such as the courtyard of a teaching building, and the surface material, vegetation, and land height had an influence on the air temperature. (4) The greater the building density in the studied area, the lower the overall wind speed, and street orientation, dense trees, and land height affected air circulation, and the wind path around the school played a role in optimizing the school wind environment. This research helps urban planners understand the environmental design around schools with the improvement of microclimates.

Cross-domain road detection based on global-local adversarial learning framework from very high resolution satellite imagery

Road detection based on convolutional neural networks (CNNs) has achieved remarkable performances for very high resolution (VHR) remote sensing images. However, this approach relies on massive annotated samples, and the problem of limited generalization for unseen images still remains. The manual pixel-level labeling process is also extremely time-consuming, and the performance of CNNs degrades significantly when there is a domain gap between the training and test images. In this paper, to address this problem, a global-local adversarial learning (GOAL) framework is proposed for cross-domain road detection. On the one hand, considering the spatial information similarities between the source and target domains, feature space driven adversarial learning is applied to explore the shared features across domains. On the other hand, the complex background of VHR remote sensing images, such as the occlusions and shadows of trees and buildings, makes some roads easy to recognize, while others are much more difficult. However, the traditional global adversarial learning approach cannot guarantee local semantic consistency. Therefore, a local alignment operation is introduced, which adaptively adjusts the weight of the adversarial loss according to the road recognition difficulty. Extensive experiments were conducted on different road datasets, including two public competition road datasets—SpaceNet and DeepGlobe—and our own large-scale annotated images from four cities: Boston, Birmingham, Shanghai, and Wuhan. The experimental results show that the proposed GOAL framework can clearly improve the cross-domain road detection performance, without any annotation of the target domain images. For instance, taking SpaceNet road dataset as the source domain, compared with the no adaptation method, the IOU performance of GOAL framework is increased by 14.36%, 5.49%, 4.51%, 5.63% and 15.14% on DeepGlobe, Boston, Birmingham, Shanghai, and Wuhan images, respectively, which demonstrates its strong generalization capability.

Water extraction of hyperspectral imagery based on a fast and effective decision tree water index

Surface water is an essential carrier for the balance of ecosystems and human existence. As applied hyperspectral remote sensing has improved, the research into water extraction from the hyperspectral image gradually attracted more attention. However, delineating water from the hyperspectral image is challenging due to shadows of buildings and trees, and other dark pixels. Furthermore, water indexes that come from multispectral imagery are directly applied to hyperspectral imagery leads to some defects, such as underutilized abundant information and higher misclassifications. Here, we proposed a decision tree water index (DTWI) method to extract water bodies from hyperspectral imagery. Combining the threshold of the near-infrared reflectance and the magnitude of the difference between reflectance of 700 and 730 nm, the DTWI method could effectively extract water from hyperspectral imagery. Three hyperspectral images derived from the study areas within the city, a mixture of city and suburban zone, and suburban zone only, were applied to test the feasibility and flexibility of this method in various sensors and environments. Four alternative methods were used to verify the robustness of the proposed method, including hyperspectral difference water index, normalized difference water index, shaded building index, and support vector machine (SVM). Our results indicated that the proposed method was better suited to extracting water bodies from hyperspectral imagery when compared with other methods and could effectively capture water on different hyperspectral sensors, various heterogeneous surroundings, and multi-spatial scales. The proposed DTWI method has high stability in threshold and band selection, which is comparable to the SVM method in the mean overall accuracy and kappa coefficient (DTWI: 0.98 and 0.81; SVM: 0.98 and 0.82). For the view of elapsed time, calculation, and applicability, the proposed method has the potential of better utilization and rapid response for water extraction in hyperspectral imagery.

A Novel Framework for Rapid Detection of Damaged Buildings Using Pre-Event LiDAR Data and Shadow Change Information

After a major earthquake in a dense urban area, the spatial distribution of heavily damaged buildings is indicative of the impact of the event on public safety. Timely assessment of the locations of severely damaged buildings and their damage morphologies using remote sensing approaches is critical for search and rescue actions. Detection of damaged buildings that did not suffer collapse can be highly challenging from aerial or satellite optical imagery, especially those structures with height-reduction or inclination damage and apparently intact roofs. A key information cue can be provided by a comparison of predicted building shadows based on pre-event building models with shadow estimates extracted from post-event imagery. This paper addresses the detection of damaged buildings in dense urban areas using the information of building shadow changes based on shadow simulation, analysis, and image processing in order to improve real-time damage detection and analysis. A novel processing framework for the rapid detection of damaged buildings without collapse is presented, which includes (a) generation of building digital surface models (DSMs) from pre-event LiDAR data, (b) building shadow detection and extraction from imagery, (c) simulation of predicted building shadows utilizing building DSMs, and (d) detection and identification of shadow areas exhibiting significant pre- and post-event differences that can be attributed to building damage. The framework is demonstrated through two simulated case studies. The building damage types considered are those typically observed in earthquake events and include height-reduction, over-turn collapse, and inclination. Total collapse cases are not addressed as these are comparatively easy to be detected using simpler algorithms. Key issues are discussed including the attributes of essential information layers and sources of error influencing the accuracy of building damage detection.

System-Independent Irradiance Sensorless ANN-Based MPPT for Photovoltaic Systems in Electric Vehicles

The integration of photovoltaic systems (PVS) in electric vehicles (EV) increases the vehicle’s autonomy by providing an additional energy source other than the battery. However, current solar cell technology generates around 200 W for a 1.4 m2 panel (to be installed on the roof of the EV) at stable irradiance conditions. This limitation in production and the sudden changes in irradiance produced by shadows of clouds, buildings, and other structures make developing a fast and efficient maximum power point tracking (MPPT) technique in this area necessary. This article proposes an artificial neural network (ANN)-based MPPT, called DS-ANN, that uses manufacturer datasheet parameters as inputs to the network to address this problem. The Bayesian backpropagation-regularization performs the training, ensuring that the MPPT technique operates satisfactorily on different PVS without retraining. We simulated the response of 20 commercial modules against actual irradiance data to validate the proposed method. The results show that our method achieves an average tracking efficiency of 99.66%, improving by 1.21% over an enhanced P&O method.

Quantifying the Effect of Building Shadowing and Cloudiness on Mean Radiant Temperature in Singapore

Improving the quality of life in urban areas has become a major concern in the last few decades. With a constantly increasing urban population and in a climate change context, detailed knowledge of the impact of urban elements on the outdoor thermal environment is relevant. In this work, we present the results of several climatic campaigns carried out in Singapore aiming to evaluate local urban climate variables. Sensors were deployed simultaneously in different sites. The effect of building shadowing in the diurnal cycle of mean radiant temperature (Tmrt) is evaluated in different seasons. Although during the Inter-Monsoon season, mean Tmrt reduction due to building shadow is ≈19 °C, during clear skies days, it can be reduced by ≈30 °C. The Tmrt difference between sites is analyzed based on the weather conditions, the sky view factor (SVF), and the type of surrounding urban elements. Under building shadow conditions, higher SVF showed higher Tmrt values, although no correlation was found between Tmrt and diffuse solar radiation (measured above the urban canopy). The results suggest a relevant contribution of other radiation components (e.g., longwave radiation). The quantitative analysis of the Tmrt provided in this work is relevant for outdoor thermal comfort strategies in tropical areas such as Singapore.

Poverty from Space: Using High Resolution Satellite Imagery for Estimating Economic Well-being

AbstractCan features extracted from high spatial resolution satellite imagery accurately estimate poverty and economic well-being? The present study investigates this question by extracting both object and texture features from satellite images of Sri Lanka. These features are used to estimate poverty rates and average expected log consumption taken from small-area estimates derived from census data, for 1,291 administrative units. Features extracted include the number and density of buildings, the prevalence of building shadows (proxying building height), the number of cars, length of roads, type of agriculture, roof material, and several texture and spectral features. A linear regression model explains between 49 and 61 percent of the variation in average expected log consumption, and between 37 and 62 percent for poverty rates. Estimates remain accurate throughout the consumption distribution, and when extrapolating predictions into adjacent areas, although performance falls when using fewer households to calculate estimates of poverty and welfare.

Multi-Scene Building Height Estimation Method Based on Shadow in High Resolution Imagery

Accurately building height estimation from remote sensing imagery is an important and challenging task. However, the existing shadow-based building height estimation methods have large errors due to the complex environment in remote sensing imagery. In this paper, we propose a multi-scene building height estimation method based on shadow in high resolution imagery. First, the shadow of building is classified and described by analyzing the features of building shadow in remote sensing imagery. Second, a variety of shadow-based building height estimation models is established in different scenes. In addition, a method of shadow regularization extraction is proposed, which can solve the problem of mutual adhesion shadows in dense building areas effectively. Finally, we propose a method for shadow length calculation combines with the fish net and the pauta criterion, which means that the large error caused by the complex shape of building shadow can be avoided. Multi-scene areas are selected for experimental analysis to prove the validity of our method. The experiment results show that the accuracy rate is as high as 96% within 2 m of absolute error of our method. In addition, we compared our proposed approach with the existing methods, and the results show that the absolute error of our method are reduced by 1.24 m–3.76 m, which can achieve high-precision estimation of building height.

Quantifying 3D building form effects on urban land surface temperature and modeling seasonal correlation patterns

Multiple factors regulate urban land surface temperature (LST), including land cover, climate, and urban form, among which urban form is now receiving more and more attention. Some studies have discussed the planar effects of urban form on LST, whereas less concern has been devoted to the vertical structure of urban areas, which can have a significant effect on heat redistribution. In this paper, we quantify the three-dimensional building form (3DBF) effects on LST captured by Landsat thermal sensors over four seasons by using a random forest (RF) regression method. The five 3DBF factors of building density (BD), building height (BH), sky view factor (SVF), frontal area index (FAI), and building shadow (BS) are calculated within a grid. The seasonal correlation of the 3DBF factors with LST is analyzed, and seven correlation patterns are modeled for the city of Wuhan in China. The results show that: 1) The best grid scale for the building form analysis in a city such as Wuhan is 180 m. 2) The 3DBF factors have a significant effect on urban LST over the four seasons. BD, BH and BS are the season-stable factors, among which BD has a heating effect, which reaches a maximum of 3.6 °C in spring, while BS has a cooling effect, which reaches −3.4 °C in winter. There is also a transition point for BH between heating and cooling at a height of 10 m. 3) SVF and FAI are season-varying factors, in which SVF has cooling effect, except in summer, while FAI also has a cooling effect, but not in winter. These findings will help us to understand how building form affects urban surface temperature, and will provide a reference for urban policy makers and planners in the future.

Research on Building Height Extraction Method from High-resolution Image

In recent years, there are many researches on calculating the height of buildings by extracting the shadow of buildings, but this method is difficult to be realized in the case of dense buildings or complex ground. To solve this problem, we can use the side information of the building to retrieve the height of the building. In this paper, Otsu Algorithm and LVQ neural network are used to extract the side information of buildings from high-resolution remote sensing images, calculate its length, and then calculate the height of the building according to the physical model of imaging. Compared with the measured building height, the calculation results of this method can meet the accuracy requirements of building height calculation, and this method can complement the results of shadow extraction method. Finally, the height information of most buildings in the area can be obtained.

WSGAN: An Improved Generative Adversarial Network for Remote Sensing Image Road Network Extraction by Weakly Supervised Processing

Road networks play an important role in navigation and city planning. However, current methods mainly adopt the supervised strategy that needs paired remote sensing images and segmentation images. These data requirements are difficult to achieve. The pair segmentation images are not easy to prepare. Thus, to alleviate the burden of acquiring large quantities of training images, this study designed an improved generative adversarial network to extract road networks through a weakly supervised process named WSGAN. The proposed method is divided into two steps: generating the mapping image and post-processing the binary image. During the generation of the mapping image, unlike other road extraction methods, this method overcomes the limitations of manually annotated segmentation images and uses mapping images that can be easily obtained from public data sets. The residual network block and Wasserstein generative adversarial network with gradient penalty loss were used in the mapping network to improve the retention of high-frequency information. In the binary image post-processing, this study used the dilation and erosion method to remove salt-and-pepper noise and obtain more accurate results. By comparing the generated road network results, the Intersection over Union scores reached 0.84, the detection accuracy of this method reached 97.83%, the precision reached 92.00%, and the recall rate reached 91.67%. The experiments used a public dataset from Google Earth screenshots. Benefiting from the powerful prediction ability of GAN, the experiments show that the proposed method performs well at extracting road networks from remote sensing images, even if the roads are covered by the shadows of buildings or trees.

Modifikasi Firefly Algorithm Untuk Partial Shading pada Photovoltaic

Shadows of buildings or trees can partially cover the PV resulting in differences in solar radiation reception. Partial shading occurs when the PV module receives different solar radiation due to shadows of buildings or trees or clouds. This condition causes the output power of the PV array to decrease. Based on the PV curve, partial shading has a direct effect, so that a decrease in voltage or current causes a decrease in the output power of the PV. Because this requires a good control device. In this study, the method was compared without control (Uncontrolled), conventional PID (PID), PID tuned by Firefly Algorithm (PID-FA), and PID tuned by Modified Firefly Algorithm (PID-MFA). From the simulation results, it is found that the best voltage is obtained from the PID-MFA controller, the best voltage obtained from the PID-MFA controller is 1.2755 pu, the best current on the PID-MFA is 4,313; 3.67; 2,551 pu, and the maximum power is 3,253 pu. Thus it can be concluded that the best controller is PID-MFA. This research can later be used as a reference and other controllers are used to obtain an optimal controller.

The impact of building height on urban thermal environment in summer: A case study of Chinese megacities.

The quantitative relationship between the spatial variation of building’s height and the associated land surface temperature (LST) change in six Chinese megacities is investigated in this paper. The six cities involved are Beijing, Shanghai, Tianjin, Chongqing, Guangzhou, and Shenzhen. Based on both remote sensing and building footprint data, we retrieved the LST using a single-channel (SC) algorithm and evaluate the heating/cooling effect caused by building-height difference via correlation analysis. The results show that the spatial distribution of high-rise buildings is mainly concentrated in the center business districts, riverside zones, and newly built-up areas of the six megacities. In the urban area, the number and the floor-area ratio of high to super high-rise buildings (>24m) account for over 5% and 4.74%, respectively. Being highly urbanized cities, most of urban areas in the six megacities are associated with high LST. Ninety-nine percent of the city areas of Shanghai, Beijing, Chongqing, Guangzhou, Shenzhen, and Tianjin are covered by the LST in the range of 30.2~67.8°C, 34.8~50.4°C, 25.3~48.3°C, 29.9~47.2°C, 27.4~43.4°C, and 33.0~48.0°C, respectively. Building’s height and LST have a negative logarithmic correlation with the correlation coefficients ranging from -0.701 to -0.853. In the building’s height within range of 0~66m, the LST will decrease significantly with the increase of building’s height. This indicates that the increase of building’s height will bring a significant cooling effect in this height range. When the building’s height exceeds 66m, its effect on LST will be greatly weakened. This is due to the influence of building shadows, local wind disturbances, and the layout of buildings.

Coupled optical-electrical-thermal analysis of a semi-transparent photovoltaic glazing façade under building shadow

The semi-transparent photovoltaic glazing (STPVG) façade can introduce comfortable daylight into the indoor space and achieve energy efficiency, which is a promising PV glazing façade system. However, it is susceptible to building shadow, reducing power generation efficiency. This paper established a coupled optical-electrical-thermal model under dynamic changing building eave shadow of the STPVG façade and built a full-scale experiment platform to test and verify the coupled model. The model was then used to simulate and analyze the electrical performance and the temperature distribution of the STPVG under different eave shadow. The results show that the I/V curve appears multi-knee shape and the P/V curve appears multi-peak shape due to the different shadow coefficient in each PV string. Furthermore, the annual overall energy performance of STPVG in Changsha, China was compared with different eave width. The transmitted solar radiation, the energy generation and energy conversion efficiency, and the total heat gain decrease with the eave width increases in the months when the shadow appears. When the eave width is 0.29 m, the monthly largest transmission loss rate is in May at 3.86%; the largest energy generation loss rate is in April at 15.3%; and the largest indoor heat gain reduction rate is in August at 3.28%. This study can provide theoretical guidance for the system optimization and engineering application of the STPVG in building energy conservation.

Comparitive Studies of Futuristic Optimisezed and Regular Solar Panels with Windload Effects on Solar Farms, Solar Panels at High Altitude for Free Green Solar Energy

Solar energy is one of the most promising green and eco-friendly renewable energy out of the wind and other non-conventional energies. In nature, we have abundance of solar energy, due to this there is a significant attention on capturing the solar energy by photovoltaic systems in recent years. Photovoltaic energy conversion is most harnessing renewable energies for different communities especially in developing countries like India. The efficiency decrease is not only due to the shadows of buildings, but also due to birds, lack of sunlight and also wind effect on solar panels which will decrease efficiency and damage the total panels or array of panels. In this work, two different analyses are carried out with changing wind velocities and angle. The aerodynamic effects on the panels were investigated by varying the orientation of panels from 0 to 180 degrees and two different wind speeds of 5m/s and 25m/s. Initial angle of 0 degrees which is normal to the flow direction is solved for two different wind speeds of 5 m/s and 25 m/s and followed by changing the orientation of panels from 0 to 180 degree inclination to investigate the aerodynamic effects on the panels. Depending on the results obtained a futuristic panel design will be proposed which can generate optimum power and also aerodynamically effective. This current study indicates that the pressure distribution on the front face of the solar panels, which are aptly suitable to design optimized solar panel shapes.

Comparison of Absorbed and Intercepted Fractions of PAR for Individual Trees Based on Radiative Transfer Model Simulations

The fraction of absorbed photosynthetically active radiation (fAPAR) is a key parameter for estimating the gross primary production (GPP) of trees. For continuous, dense forest canopies, fAPAR, is often equated with the intercepted fraction, fIPAR. This assumption is not valid for individual trees in urban environments or parkland settings where the canopy is sparse and there are well-defined tree crown boundaries. Here, the distinction between fAPAR and fIPAR can be strongly influenced by the background and large illumination variations due to multi-scattering and shadows of buildings. This study investigates the radiative budget of PAR bands using a coupled leaf-canopy radiative transfer model (PROSPECT-DART), considering a suite of tropical tree species over a wide range of assumed leaf chlorophyll contents. The analyses simulate hyperspectral images (5 nm bandwidth) of individual tree crowns for the selected background (concrete vs. grass) and illumination conditions. We then use an artificial neural network-based method to partition sunlit vs. shaded leaves within each crown, as the latter have lower fAPAR and fIPAR values. Our results show fAPAR of sunlit leaves decreases with the ratio of diffuse to direct scene irradiance (SKYL), while SKYL has minimal influence for shaded leaves. Both fAPAR and fIPAR decrease at more oblique solar zenith angles (SZA). Higher values of fAPAR and fIPAR occur with concrete backgrounds and the influence of the background is larger at higher diffuse ratio and solar zenith angles. The results show that fIPAR is typically 6–9% higher than fAPAR, and up to 14% higher for sunlit leaves with a concrete background at SKYL = 0. The differences between the fIPAR and fAPAR also depend on the health condition of the leaves, such as chlorophyll content. This study can improve the understanding of urban individual trees fAPAR/fIPAR and facilitate the development of protocols for fAPAR field measurements.

Determination of optimum tilt and azimuth angle of BiSPVT system along with its performance due to shadow of adjacent buildings

Development of a BIPV system with appropriate azimuth and tilt angle on top of a suitable existing or new building in urban areas is a possible option to generate both electrical and thermal energies and also to create an additional living space. These azimuth and tilt angles are significantly influenced by the shadow of the neighborhood’s buildings. The relative height, plan orientation, plan area, position, and horizontal distance of the neighborhood’s buildings are critical parameters for determining optimum azimuth and tilt angle to receive maximum insolation. Present proposed model accurately calculates these angels and the energy, and the model also works well when water flows over the BIPV panel from an overhead tank through gravity with an aim to increase the efficiency of PV panel. The periodic nature of relevant parameters is considered in the thermal modeling of the BiSPVT system. Results indicate that the optimum azimuth angle from south-facing increases with a rise in the latitude angle of the place. The optimum tilt angle depends on the corresponding latitude and the different contributing parts of total insolation. The electrical output of a south-facing BiSPVT system with a tilt angle equal to latitude is increased by 3.7% when BiSPVT system is installed at optimum azimuth and tilt angle. The output is further enhanced by 4.3% when water is flown at the rate of 1.1 kgs−1 over the PV panel.

Mozambique Flood (2019) Caused by Tropical Cyclone Idai Monitored From Sentinel-1 and Sentinel-2 Images

The tropical cyclone Idai caused severe floods in Mozambique in March 2019. Sentinel-1 and Sentinel-2 images are processed to monitor the flood disaster in Pungue and Buzi rivers by combining the object-based image analysis approach and the decision tree algorithm. Water is preliminarily extracted from Sentinel-1 image, and shadows of mountain and buildings in the study area are extracted from Sentinel-2 image. Mountain shadow is extracted based on the decision tree classification rules constructed by the digital elevation model and the index model, while building shadow is extracted by constructing the decision tree classification rules using the features of objects. Water extraction results are combined with shadows to eliminate confusing shadows in the preliminary extraction to obtain the accurate flood extent. Sentinel-2 images are used to classify land use types in multiple periods before and after the flood and analyze the disaster by combining with the change information of the water. Land use type changes are analyzed and predicted with the CA-Markov model. The results show that the maximum submerged area was 3.5 times of the normal water area, and that most areas were submerged for more than 10 d. The area between Pungue and Buzi rivers were severely affected, where a large number of crops and villages were submerged. Grassland and cropland were the most seriously submerged. The overall accuracy of extracted water results ranges from 86% to 92%. The results indicated that the combining method can effectively suppress the influence of shadows on water extraction results.

Urban Black-Odor Water Remote Sensing Mapping Based on Shadow Removal: A Case Study in Nanjing

Urban black-odor water is a serious water environmental problem worldwide that seriously affects the living environment and the health of residents. The detection of black-odor water is a primary problem of centralized governance. Remote sensing is an effective means for water-quality monitoring. However, in most urban areas, with their complex underlying conditions, black-odor water always shows an uncertain distribution and small area. Furthermore, because of similar spectral characteristics, it is easily confused with dark objects such as asphalt roads, dark roofs, bridges, river levees, and building shadows. In this article, we propose a new black-odor water detection method, without shadow misjudgment, for complex urban conditions. A remote sensing image from the Chinese high-resolution satellite Gaofen-2 (GF-2) was used for detection. Using in situ water-quality measurement data for verification, the results showed that the proposed method achieved an accuracy of 85.7% while reducing the false alarm rate to 3.5%. Compared to four other existing methods, the proposed method proved to be more practical because it can reduce the false extraction of nonblack-odor water to the greatest extent possible while ensuring accuracy The method proposed in this article has a good effect on the extraction of black-odor water in Nanjing City. Therefore, our method can be applied widely to screening and management of large-scale urban black-odor water and can provide reliable data for centralized treatment.

Analysis of landscape microclimate and green space transformation strategy for old residential areas——case: Luoliu community

As an old community, Luoliu community is representative in Shanghai. Traditional infrastructure is not combined with public space and green space layout, which makes the climate resilience of the old residential area urgently need to be improved. Through questionnaire survey, field measurement, combined with Envi-met to simulate microclimate changes and formulated green space resilience transformation strategies. Microclimate factors such as temperature, humidity, wind speed and wind direction change with the spatial distribution and vegetation structure. First, the walls of buildings are most vulnerable to the shadow of buildings. The microclimate of the building enclosed green space is stable, but its toughness is poor. Parallel green space can restrain the temperature rise to a certain extent, it has strong space toughness. Green enclosure space is also closed, usually covered with green plants. Although it can reduce the impact of solar radiation, overgrown and untrimmed trees will become an obstacle to air ducts. Ventilation should be the priority. Second, the canopy and vertical structure of green plants should be reasonable. Third, the combination layout of the dominant wind direction and the residential complex shall be considered comprehensively to plan the public facilities and sidewalks.